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基于音频视频决策融合处理的单独个体的多式降落检测
Shiqin Jiao1, Guoqi Li1, Guiyang Zhang1
1School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China.
Heliyon
|April 29, 2024
概括
本研究介绍了一种使用音频和视频的多式降落检测系统. 融合式方法显著提高了落检测的准确性,为老年人安全提供了更可靠的解决方案.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 布对安全构成重大风险,尤其对单独生活的老年人来说.
- 现有的落检测系统经常与环境变化 (如照明不良或监测范围有限) 进行斗争.
- 需要强大可靠的摔倒检测系统,能够克服单模式方法的局限性.
研究的目的:
- 开发和评估一个综合音频和视频数据的多式降落检测系统.
- 与单一模式方法相比,提高掉落检测的准确性和可靠性.
- 提供一种非侵入性和有效的解决方案,用于在现实环境中检测落.
主要方法:
- 一个基于视频的模型使用YOLOv7-Pose进行骨架关节提取,然后进行两流空间时间图卷积网络 (ST-GCN) 进行分类.
- 一个基于音频的模型,采用日志尺度的Mel光谱图,并通过MobileNetV2架构进行处理.
- 视频和音频模型的决策融合使用线性权重和Dempster-Shafer (D-S) 理论.
主要成果:
- 多式联络系统在摔倒检测方面显著优于单式联络方法.
- 灵敏度从81.67% (仅视频) 增加到96.67% (线性加权) 和97.50% (D-S理论).
- 融合方法在复杂的日常生活环境中显示出更高的可靠性和有效性.
结论:
- 音频和视频数据的多式融合为落检测提供了卓越的解决方案.
- 拟议的系统有效地解决了传统基于视频的方法的局限性,提高了准确性和稳定性.
- 视听融合是未来的摔倒检测系统的一个有希望的方向,特别是对于弱势群体.
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